Semiautomated Generation of Logic Rules for Tabular Information in Building Codes to Support Automated Code Compliance Checking

2022 ◽  
Vol 36 (1) ◽  
Author(s):  
Xiaorui Xue ◽  
Jin Wu ◽  
Jiansong Zhang
Keyword(s):  
Building Codes ◽  
Compliance Checking ◽  
Code Compliance

A Proposal of Data Generation Guide for openBIM-based Building Code Compliance Checking

2021 ◽  
Vol 26 (2) ◽  
pp. 112-121
Author(s):  
Inhan Kim ◽  
Sejin Lee ◽  
Jiyoung Kim ◽  
Ahjin Lee ◽  
Jungsik Choi
Keyword(s):  
Building Code ◽  
Data Generation ◽  
Compliance Checking ◽  
Code Compliance

Code compliance checking by using derived objects

2012 ◽  
Vol 11 (2) ◽  
Author(s):  
E.J. Jin ◽  
J.H. Garrett ◽  
B. Akinci
Keyword(s):  
Compliance Checking ◽  
Code Compliance

scholarly journals A Generalized Adaptive Framework (GAF) for Automating Code Compliance Checking

Buildings ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 86 ◽  
Author(s):  
Nawari
Keyword(s):  
Review Process ◽  
Building Design ◽  
Theoretical Background ◽  
Design Review ◽  
Data Standard ◽  
Generalized Framework ◽  
Compliance Checking ◽  
Adaptive Framework ◽  
Verification Process ◽  
Code Compliance

Building design review is the procedure of checking a design against codes and standard provisions to satisfy the accuracy of the design and identify non-compliances before construction begins. The current approaches for conducting the design review process in an automatic or semi-automatic manner are either based on proprietary, domain-specific or hard-coded rule-based mechanisms. These methods may be effective in their specific applications, but they have the downsides of being costly to maintain, inflexible to modify, and lack a generalized framework of rules and regulations modeling that can adapt to various engineering design realms, and thus don’t support a neutral data standard. They are often referred to as 'Black Box' or ‘Gray Box’ approaches. This research offers a new comprehensive framework that reduces the limitations of the cited methods. Building regulations, for instance, are legal documents transcribed and approved by professionals to be interpreted and applied by people. They are hardly as precise as formal logic. Engineers, architects, and contractors can read those technical documents and transform them into scientific notations and software applications. They can extract any data they need, reason about it, and apply it at various phases of the project. How these extraction and use are carried out is a critical component of automating the design review process. The chief goal is to address this issue by developing a Generalized Adaptive Framework (GAF) for a neutral data standard (Industry Foundation Classes (IFC)) that enables automating the code compliance checking processes to achieve design efficiency and cost-effectiveness. The objectives of this study comprise i) to develop a theoretical background to an adaptive framework that supports a neutral data standard for transforming the written code regulations and rules into a computable model, and ii) to define the various modules required for computerizing of the code compliance verification process.

scholarly journals Joint Extraction of Multiple Relations and Entities from Building Code Clauses

2020 ◽  
Vol 10 (20) ◽  
pp. 7103
Author(s):  
Fulin Li ◽  
Yuanbin Song ◽  
Yongwei Shan
Keyword(s):  
Learning Algorithm ◽  
Semantic Relations ◽  
Named Entities ◽  
Domain Specific ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
Compliance Checking ◽  
Extraction Model ◽  
Code Compliance ◽  
Engineering Constraints

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes.

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